Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Wednesday 12 August 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 205,129 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 205,119 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
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## 69 2020-05-08 East of England 33
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## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
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## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
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## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
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## 108 2020-06-16 East of England 3
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## 125 2020-07-03 East of England 0
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## 130 2020-07-08 East of England 0
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## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 7
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
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## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 1
## 144 2020-07-22 East of England 1
## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-07-26 East of England 1
## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 1
## 151 2020-07-29 East of England 0
## 152 2020-07-30 East of England 0
## 153 2020-07-31 East of England 1
## 154 2020-08-01 East of England 0
## 155 2020-08-02 East of England 0
## 156 2020-08-03 East of England 0
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## 495 2020-03-06 North East and Yorkshire 0
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## 540 2020-04-20 North East and Yorkshire 100
## 541 2020-04-21 North East and Yorkshire 76
## 542 2020-04-22 North East and Yorkshire 84
## 543 2020-04-23 North East and Yorkshire 63
## 544 2020-04-24 North East and Yorkshire 72
## 545 2020-04-25 North East and Yorkshire 69
## 546 2020-04-26 North East and Yorkshire 65
## 547 2020-04-27 North East and Yorkshire 65
## 548 2020-04-28 North East and Yorkshire 57
## 549 2020-04-29 North East and Yorkshire 69
## 550 2020-04-30 North East and Yorkshire 57
## 551 2020-05-01 North East and Yorkshire 64
## 552 2020-05-02 North East and Yorkshire 48
## 553 2020-05-03 North East and Yorkshire 40
## 554 2020-05-04 North East and Yorkshire 49
## 555 2020-05-05 North East and Yorkshire 40
## 556 2020-05-06 North East and Yorkshire 51
## 557 2020-05-07 North East and Yorkshire 45
## 558 2020-05-08 North East and Yorkshire 42
## 559 2020-05-09 North East and Yorkshire 44
## 560 2020-05-10 North East and Yorkshire 40
## 561 2020-05-11 North East and Yorkshire 29
## 562 2020-05-12 North East and Yorkshire 27
## 563 2020-05-13 North East and Yorkshire 28
## 564 2020-05-14 North East and Yorkshire 31
## 565 2020-05-15 North East and Yorkshire 32
## 566 2020-05-16 North East and Yorkshire 35
## 567 2020-05-17 North East and Yorkshire 26
## 568 2020-05-18 North East and Yorkshire 30
## 569 2020-05-19 North East and Yorkshire 27
## 570 2020-05-20 North East and Yorkshire 22
## 571 2020-05-21 North East and Yorkshire 33
## 572 2020-05-22 North East and Yorkshire 22
## 573 2020-05-23 North East and Yorkshire 18
## 574 2020-05-24 North East and Yorkshire 26
## 575 2020-05-25 North East and Yorkshire 21
## 576 2020-05-26 North East and Yorkshire 21
## 577 2020-05-27 North East and Yorkshire 22
## 578 2020-05-28 North East and Yorkshire 21
## 579 2020-05-29 North East and Yorkshire 25
## 580 2020-05-30 North East and Yorkshire 20
## 581 2020-05-31 North East and Yorkshire 20
## 582 2020-06-01 North East and Yorkshire 17
## 583 2020-06-02 North East and Yorkshire 23
## 584 2020-06-03 North East and Yorkshire 23
## 585 2020-06-04 North East and Yorkshire 17
## 586 2020-06-05 North East and Yorkshire 18
## 587 2020-06-06 North East and Yorkshire 21
## 588 2020-06-07 North East and Yorkshire 14
## 589 2020-06-08 North East and Yorkshire 11
## 590 2020-06-09 North East and Yorkshire 12
## 591 2020-06-10 North East and Yorkshire 19
## 592 2020-06-11 North East and Yorkshire 7
## 593 2020-06-12 North East and Yorkshire 9
## 594 2020-06-13 North East and Yorkshire 10
## 595 2020-06-14 North East and Yorkshire 11
## 596 2020-06-15 North East and Yorkshire 9
## 597 2020-06-16 North East and Yorkshire 10
## 598 2020-06-17 North East and Yorkshire 9
## 599 2020-06-18 North East and Yorkshire 11
## 600 2020-06-19 North East and Yorkshire 6
## 601 2020-06-20 North East and Yorkshire 5
## 602 2020-06-21 North East and Yorkshire 4
## 603 2020-06-22 North East and Yorkshire 7
## 604 2020-06-23 North East and Yorkshire 8
## 605 2020-06-24 North East and Yorkshire 10
## 606 2020-06-25 North East and Yorkshire 4
## 607 2020-06-26 North East and Yorkshire 7
## 608 2020-06-27 North East and Yorkshire 4
## 609 2020-06-28 North East and Yorkshire 5
## 610 2020-06-29 North East and Yorkshire 2
## 611 2020-06-30 North East and Yorkshire 7
## 612 2020-07-01 North East and Yorkshire 1
## 613 2020-07-02 North East and Yorkshire 4
## 614 2020-07-03 North East and Yorkshire 4
## 615 2020-07-04 North East and Yorkshire 4
## 616 2020-07-05 North East and Yorkshire 3
## 617 2020-07-06 North East and Yorkshire 2
## 618 2020-07-07 North East and Yorkshire 3
## 619 2020-07-08 North East and Yorkshire 3
## 620 2020-07-09 North East and Yorkshire 0
## 621 2020-07-10 North East and Yorkshire 3
## 622 2020-07-11 North East and Yorkshire 1
## 623 2020-07-12 North East and Yorkshire 4
## 624 2020-07-13 North East and Yorkshire 1
## 625 2020-07-14 North East and Yorkshire 1
## 626 2020-07-15 North East and Yorkshire 2
## 627 2020-07-16 North East and Yorkshire 3
## 628 2020-07-17 North East and Yorkshire 1
## 629 2020-07-18 North East and Yorkshire 2
## 630 2020-07-19 North East and Yorkshire 2
## 631 2020-07-20 North East and Yorkshire 1
## 632 2020-07-21 North East and Yorkshire 1
## 633 2020-07-22 North East and Yorkshire 6
## 634 2020-07-23 North East and Yorkshire 0
## 635 2020-07-24 North East and Yorkshire 1
## 636 2020-07-25 North East and Yorkshire 5
## 637 2020-07-26 North East and Yorkshire 1
## 638 2020-07-27 North East and Yorkshire 0
## 639 2020-07-28 North East and Yorkshire 2
## 640 2020-07-29 North East and Yorkshire 1
## 641 2020-07-30 North East and Yorkshire 0
## 642 2020-07-31 North East and Yorkshire 1
## 643 2020-08-01 North East and Yorkshire 3
## 644 2020-08-02 North East and Yorkshire 2
## 645 2020-08-03 North East and Yorkshire 1
## 646 2020-08-04 North East and Yorkshire 1
## 647 2020-08-05 North East and Yorkshire 1
## 648 2020-08-06 North East and Yorkshire 4
## 649 2020-08-07 North East and Yorkshire 0
## 650 2020-08-08 North East and Yorkshire 1
## 651 2020-08-09 North East and Yorkshire 2
## 652 2020-08-10 North East and Yorkshire 0
## 653 2020-03-01 North West 0
## 654 2020-03-02 North West 0
## 655 2020-03-03 North West 0
## 656 2020-03-04 North West 0
## 657 2020-03-05 North West 1
## 658 2020-03-06 North West 0
## 659 2020-03-07 North West 0
## 660 2020-03-08 North West 1
## 661 2020-03-09 North West 0
## 662 2020-03-10 North West 0
## 663 2020-03-11 North West 0
## 664 2020-03-12 North West 2
## 665 2020-03-13 North West 3
## 666 2020-03-14 North West 1
## 667 2020-03-15 North West 4
## 668 2020-03-16 North West 2
## 669 2020-03-17 North West 4
## 670 2020-03-18 North West 6
## 671 2020-03-19 North West 7
## 672 2020-03-20 North West 10
## 673 2020-03-21 North West 11
## 674 2020-03-22 North West 13
## 675 2020-03-23 North West 15
## 676 2020-03-24 North West 21
## 677 2020-03-25 North West 21
## 678 2020-03-26 North West 29
## 679 2020-03-27 North West 36
## 680 2020-03-28 North West 28
## 681 2020-03-29 North West 46
## 682 2020-03-30 North West 67
## 683 2020-03-31 North West 52
## 684 2020-04-01 North West 86
## 685 2020-04-02 North West 96
## 686 2020-04-03 North West 95
## 687 2020-04-04 North West 98
## 688 2020-04-05 North West 102
## 689 2020-04-06 North West 100
## 690 2020-04-07 North West 135
## 691 2020-04-08 North West 127
## 692 2020-04-09 North West 119
## 693 2020-04-10 North West 117
## 694 2020-04-11 North West 138
## 695 2020-04-12 North West 125
## 696 2020-04-13 North West 129
## 697 2020-04-14 North West 131
## 698 2020-04-15 North West 114
## 699 2020-04-16 North West 135
## 700 2020-04-17 North West 98
## 701 2020-04-18 North West 113
## 702 2020-04-19 North West 71
## 703 2020-04-20 North West 83
## 704 2020-04-21 North West 76
## 705 2020-04-22 North West 86
## 706 2020-04-23 North West 85
## 707 2020-04-24 North West 66
## 708 2020-04-25 North West 66
## 709 2020-04-26 North West 55
## 710 2020-04-27 North West 54
## 711 2020-04-28 North West 57
## 712 2020-04-29 North West 63
## 713 2020-04-30 North West 59
## 714 2020-05-01 North West 45
## 715 2020-05-02 North West 56
## 716 2020-05-03 North West 55
## 717 2020-05-04 North West 48
## 718 2020-05-05 North West 48
## 719 2020-05-06 North West 44
## 720 2020-05-07 North West 49
## 721 2020-05-08 North West 42
## 722 2020-05-09 North West 31
## 723 2020-05-10 North West 42
## 724 2020-05-11 North West 35
## 725 2020-05-12 North West 38
## 726 2020-05-13 North West 25
## 727 2020-05-14 North West 26
## 728 2020-05-15 North West 33
## 729 2020-05-16 North West 32
## 730 2020-05-17 North West 24
## 731 2020-05-18 North West 31
## 732 2020-05-19 North West 35
## 733 2020-05-20 North West 27
## 734 2020-05-21 North West 27
## 735 2020-05-22 North West 26
## 736 2020-05-23 North West 31
## 737 2020-05-24 North West 26
## 738 2020-05-25 North West 31
## 739 2020-05-26 North West 27
## 740 2020-05-27 North West 27
## 741 2020-05-28 North West 28
## 742 2020-05-29 North West 20
## 743 2020-05-30 North West 19
## 744 2020-05-31 North West 13
## 745 2020-06-01 North West 12
## 746 2020-06-02 North West 27
## 747 2020-06-03 North West 22
## 748 2020-06-04 North West 22
## 749 2020-06-05 North West 16
## 750 2020-06-06 North West 26
## 751 2020-06-07 North West 20
## 752 2020-06-08 North West 23
## 753 2020-06-09 North West 17
## 754 2020-06-10 North West 16
## 755 2020-06-11 North West 16
## 756 2020-06-12 North West 11
## 757 2020-06-13 North West 10
## 758 2020-06-14 North West 15
## 759 2020-06-15 North West 16
## 760 2020-06-16 North West 15
## 761 2020-06-17 North West 13
## 762 2020-06-18 North West 14
## 763 2020-06-19 North West 7
## 764 2020-06-20 North West 11
## 765 2020-06-21 North West 8
## 766 2020-06-22 North West 11
## 767 2020-06-23 North West 13
## 768 2020-06-24 North West 13
## 769 2020-06-25 North West 15
## 770 2020-06-26 North West 6
## 771 2020-06-27 North West 7
## 772 2020-06-28 North West 9
## 773 2020-06-29 North West 9
## 774 2020-06-30 North West 7
## 775 2020-07-01 North West 3
## 776 2020-07-02 North West 6
## 777 2020-07-03 North West 7
## 778 2020-07-04 North West 4
## 779 2020-07-05 North West 6
## 780 2020-07-06 North West 9
## 781 2020-07-07 North West 8
## 782 2020-07-08 North West 5
## 783 2020-07-09 North West 10
## 784 2020-07-10 North West 2
## 785 2020-07-11 North West 5
## 786 2020-07-12 North West 0
## 787 2020-07-13 North West 6
## 788 2020-07-14 North West 4
## 789 2020-07-15 North West 5
## 790 2020-07-16 North West 2
## 791 2020-07-17 North West 4
## 792 2020-07-18 North West 4
## 793 2020-07-19 North West 3
## 794 2020-07-20 North West 0
## 795 2020-07-21 North West 2
## 796 2020-07-22 North West 3
## 797 2020-07-23 North West 2
## 798 2020-07-24 North West 1
## 799 2020-07-25 North West 0
## 800 2020-07-26 North West 3
## 801 2020-07-27 North West 1
## 802 2020-07-28 North West 1
## 803 2020-07-29 North West 2
## 804 2020-07-30 North West 1
## 805 2020-07-31 North West 0
## 806 2020-08-01 North West 2
## 807 2020-08-02 North West 0
## 808 2020-08-03 North West 7
## 809 2020-08-04 North West 3
## 810 2020-08-05 North West 1
## 811 2020-08-06 North West 1
## 812 2020-08-07 North West 0
## 813 2020-08-08 North West 0
## 814 2020-08-09 North West 1
## 815 2020-08-10 North West 0
## 816 2020-03-01 South East 0
## 817 2020-03-02 South East 0
## 818 2020-03-03 South East 1
## 819 2020-03-04 South East 0
## 820 2020-03-05 South East 1
## 821 2020-03-06 South East 0
## 822 2020-03-07 South East 0
## 823 2020-03-08 South East 1
## 824 2020-03-09 South East 1
## 825 2020-03-10 South East 1
## 826 2020-03-11 South East 1
## 827 2020-03-12 South East 0
## 828 2020-03-13 South East 1
## 829 2020-03-14 South East 1
## 830 2020-03-15 South East 5
## 831 2020-03-16 South East 8
## 832 2020-03-17 South East 7
## 833 2020-03-18 South East 10
## 834 2020-03-19 South East 9
## 835 2020-03-20 South East 13
## 836 2020-03-21 South East 7
## 837 2020-03-22 South East 25
## 838 2020-03-23 South East 20
## 839 2020-03-24 South East 22
## 840 2020-03-25 South East 29
## 841 2020-03-26 South East 35
## 842 2020-03-27 South East 34
## 843 2020-03-28 South East 36
## 844 2020-03-29 South East 55
## 845 2020-03-30 South East 58
## 846 2020-03-31 South East 65
## 847 2020-04-01 South East 66
## 848 2020-04-02 South East 55
## 849 2020-04-03 South East 72
## 850 2020-04-04 South East 80
## 851 2020-04-05 South East 82
## 852 2020-04-06 South East 88
## 853 2020-04-07 South East 100
## 854 2020-04-08 South East 83
## 855 2020-04-09 South East 104
## 856 2020-04-10 South East 88
## 857 2020-04-11 South East 88
## 858 2020-04-12 South East 88
## 859 2020-04-13 South East 84
## 860 2020-04-14 South East 65
## 861 2020-04-15 South East 72
## 862 2020-04-16 South East 56
## 863 2020-04-17 South East 86
## 864 2020-04-18 South East 57
## 865 2020-04-19 South East 70
## 866 2020-04-20 South East 87
## 867 2020-04-21 South East 51
## 868 2020-04-22 South East 54
## 869 2020-04-23 South East 57
## 870 2020-04-24 South East 64
## 871 2020-04-25 South East 51
## 872 2020-04-26 South East 51
## 873 2020-04-27 South East 41
## 874 2020-04-28 South East 40
## 875 2020-04-29 South East 47
## 876 2020-04-30 South East 29
## 877 2020-05-01 South East 37
## 878 2020-05-02 South East 36
## 879 2020-05-03 South East 17
## 880 2020-05-04 South East 35
## 881 2020-05-05 South East 29
## 882 2020-05-06 South East 25
## 883 2020-05-07 South East 27
## 884 2020-05-08 South East 26
## 885 2020-05-09 South East 28
## 886 2020-05-10 South East 19
## 887 2020-05-11 South East 25
## 888 2020-05-12 South East 27
## 889 2020-05-13 South East 18
## 890 2020-05-14 South East 32
## 891 2020-05-15 South East 25
## 892 2020-05-16 South East 22
## 893 2020-05-17 South East 18
## 894 2020-05-18 South East 22
## 895 2020-05-19 South East 12
## 896 2020-05-20 South East 22
## 897 2020-05-21 South East 15
## 898 2020-05-22 South East 17
## 899 2020-05-23 South East 21
## 900 2020-05-24 South East 17
## 901 2020-05-25 South East 13
## 902 2020-05-26 South East 19
## 903 2020-05-27 South East 19
## 904 2020-05-28 South East 12
## 905 2020-05-29 South East 22
## 906 2020-05-30 South East 8
## 907 2020-05-31 South East 12
## 908 2020-06-01 South East 11
## 909 2020-06-02 South East 13
## 910 2020-06-03 South East 18
## 911 2020-06-04 South East 11
## 912 2020-06-05 South East 11
## 913 2020-06-06 South East 10
## 914 2020-06-07 South East 12
## 915 2020-06-08 South East 8
## 916 2020-06-09 South East 10
## 917 2020-06-10 South East 11
## 918 2020-06-11 South East 5
## 919 2020-06-12 South East 6
## 920 2020-06-13 South East 7
## 921 2020-06-14 South East 7
## 922 2020-06-15 South East 8
## 923 2020-06-16 South East 14
## 924 2020-06-17 South East 9
## 925 2020-06-18 South East 4
## 926 2020-06-19 South East 7
## 927 2020-06-20 South East 5
## 928 2020-06-21 South East 3
## 929 2020-06-22 South East 2
## 930 2020-06-23 South East 8
## 931 2020-06-24 South East 7
## 932 2020-06-25 South East 5
## 933 2020-06-26 South East 8
## 934 2020-06-27 South East 9
## 935 2020-06-28 South East 6
## 936 2020-06-29 South East 5
## 937 2020-06-30 South East 5
## 938 2020-07-01 South East 2
## 939 2020-07-02 South East 8
## 940 2020-07-03 South East 3
## 941 2020-07-04 South East 6
## 942 2020-07-05 South East 5
## 943 2020-07-06 South East 4
## 944 2020-07-07 South East 6
## 945 2020-07-08 South East 3
## 946 2020-07-09 South East 7
## 947 2020-07-10 South East 3
## 948 2020-07-11 South East 4
## 949 2020-07-12 South East 4
## 950 2020-07-13 South East 5
## 951 2020-07-14 South East 5
## 952 2020-07-15 South East 6
## 953 2020-07-16 South East 3
## 954 2020-07-17 South East 1
## 955 2020-07-18 South East 5
## 956 2020-07-19 South East 2
## 957 2020-07-20 South East 6
## 958 2020-07-21 South East 4
## 959 2020-07-22 South East 2
## 960 2020-07-23 South East 3
## 961 2020-07-24 South East 1
## 962 2020-07-25 South East 1
## 963 2020-07-26 South East 3
## 964 2020-07-27 South East 0
## 965 2020-07-28 South East 3
## 966 2020-07-29 South East 2
## 967 2020-07-30 South East 3
## 968 2020-07-31 South East 1
## 969 2020-08-01 South East 2
## 970 2020-08-02 South East 3
## 971 2020-08-03 South East 0
## 972 2020-08-04 South East 0
## 973 2020-08-05 South East 0
## 974 2020-08-06 South East 0
## 975 2020-08-07 South East 0
## 976 2020-08-08 South East 1
## 977 2020-08-09 South East 0
## 978 2020-08-10 South East 0
## 979 2020-03-01 South West 0
## 980 2020-03-02 South West 0
## 981 2020-03-03 South West 0
## 982 2020-03-04 South West 0
## 983 2020-03-05 South West 0
## 984 2020-03-06 South West 0
## 985 2020-03-07 South West 0
## 986 2020-03-08 South West 0
## 987 2020-03-09 South West 0
## 988 2020-03-10 South West 0
## 989 2020-03-11 South West 1
## 990 2020-03-12 South West 0
## 991 2020-03-13 South West 0
## 992 2020-03-14 South West 1
## 993 2020-03-15 South West 0
## 994 2020-03-16 South West 0
## 995 2020-03-17 South West 2
## 996 2020-03-18 South West 2
## 997 2020-03-19 South West 4
## 998 2020-03-20 South West 3
## 999 2020-03-21 South West 6
## 1000 2020-03-22 South West 7
## 1001 2020-03-23 South West 8
## 1002 2020-03-24 South West 7
## 1003 2020-03-25 South West 9
## 1004 2020-03-26 South West 11
## 1005 2020-03-27 South West 13
## 1006 2020-03-28 South West 21
## 1007 2020-03-29 South West 18
## 1008 2020-03-30 South West 23
## 1009 2020-03-31 South West 23
## 1010 2020-04-01 South West 21
## 1011 2020-04-02 South West 23
## 1012 2020-04-03 South West 30
## 1013 2020-04-04 South West 42
## 1014 2020-04-05 South West 32
## 1015 2020-04-06 South West 34
## 1016 2020-04-07 South West 39
## 1017 2020-04-08 South West 47
## 1018 2020-04-09 South West 24
## 1019 2020-04-10 South West 46
## 1020 2020-04-11 South West 43
## 1021 2020-04-12 South West 23
## 1022 2020-04-13 South West 27
## 1023 2020-04-14 South West 24
## 1024 2020-04-15 South West 32
## 1025 2020-04-16 South West 29
## 1026 2020-04-17 South West 33
## 1027 2020-04-18 South West 25
## 1028 2020-04-19 South West 31
## 1029 2020-04-20 South West 26
## 1030 2020-04-21 South West 26
## 1031 2020-04-22 South West 23
## 1032 2020-04-23 South West 17
## 1033 2020-04-24 South West 19
## 1034 2020-04-25 South West 15
## 1035 2020-04-26 South West 27
## 1036 2020-04-27 South West 13
## 1037 2020-04-28 South West 17
## 1038 2020-04-29 South West 15
## 1039 2020-04-30 South West 26
## 1040 2020-05-01 South West 6
## 1041 2020-05-02 South West 7
## 1042 2020-05-03 South West 10
## 1043 2020-05-04 South West 17
## 1044 2020-05-05 South West 14
## 1045 2020-05-06 South West 19
## 1046 2020-05-07 South West 16
## 1047 2020-05-08 South West 6
## 1048 2020-05-09 South West 11
## 1049 2020-05-10 South West 5
## 1050 2020-05-11 South West 8
## 1051 2020-05-12 South West 7
## 1052 2020-05-13 South West 7
## 1053 2020-05-14 South West 6
## 1054 2020-05-15 South West 4
## 1055 2020-05-16 South West 4
## 1056 2020-05-17 South West 6
## 1057 2020-05-18 South West 4
## 1058 2020-05-19 South West 6
## 1059 2020-05-20 South West 1
## 1060 2020-05-21 South West 9
## 1061 2020-05-22 South West 7
## 1062 2020-05-23 South West 6
## 1063 2020-05-24 South West 3
## 1064 2020-05-25 South West 8
## 1065 2020-05-26 South West 11
## 1066 2020-05-27 South West 5
## 1067 2020-05-28 South West 10
## 1068 2020-05-29 South West 7
## 1069 2020-05-30 South West 3
## 1070 2020-05-31 South West 2
## 1071 2020-06-01 South West 7
## 1072 2020-06-02 South West 2
## 1073 2020-06-03 South West 7
## 1074 2020-06-04 South West 2
## 1075 2020-06-05 South West 2
## 1076 2020-06-06 South West 1
## 1077 2020-06-07 South West 3
## 1078 2020-06-08 South West 3
## 1079 2020-06-09 South West 0
## 1080 2020-06-10 South West 1
## 1081 2020-06-11 South West 2
## 1082 2020-06-12 South West 2
## 1083 2020-06-13 South West 2
## 1084 2020-06-14 South West 0
## 1085 2020-06-15 South West 2
## 1086 2020-06-16 South West 2
## 1087 2020-06-17 South West 0
## 1088 2020-06-18 South West 0
## 1089 2020-06-19 South West 0
## 1090 2020-06-20 South West 2
## 1091 2020-06-21 South West 0
## 1092 2020-06-22 South West 1
## 1093 2020-06-23 South West 1
## 1094 2020-06-24 South West 1
## 1095 2020-06-25 South West 0
## 1096 2020-06-26 South West 3
## 1097 2020-06-27 South West 0
## 1098 2020-06-28 South West 0
## 1099 2020-06-29 South West 1
## 1100 2020-06-30 South West 0
## 1101 2020-07-01 South West 0
## 1102 2020-07-02 South West 0
## 1103 2020-07-03 South West 0
## 1104 2020-07-04 South West 0
## 1105 2020-07-05 South West 1
## 1106 2020-07-06 South West 0
## 1107 2020-07-07 South West 0
## 1108 2020-07-08 South West 2
## 1109 2020-07-09 South West 0
## 1110 2020-07-10 South West 1
## 1111 2020-07-11 South West 0
## 1112 2020-07-12 South West 0
## 1113 2020-07-13 South West 1
## 1114 2020-07-14 South West 0
## 1115 2020-07-15 South West 0
## 1116 2020-07-16 South West 0
## 1117 2020-07-17 South West 1
## 1118 2020-07-18 South West 0
## 1119 2020-07-19 South West 0
## 1120 2020-07-20 South West 0
## 1121 2020-07-21 South West 0
## 1122 2020-07-22 South West 0
## 1123 2020-07-23 South West 0
## 1124 2020-07-24 South West 0
## 1125 2020-07-25 South West 0
## 1126 2020-07-26 South West 0
## 1127 2020-07-27 South West 0
## 1128 2020-07-28 South West 0
## 1129 2020-07-29 South West 0
## 1130 2020-07-30 South West 1
## 1131 2020-07-31 South West 0
## 1132 2020-08-01 South West 0
## 1133 2020-08-02 South West 0
## 1134 2020-08-03 South West 0
## 1135 2020-08-04 South West 0
## 1136 2020-08-05 South West 0
## 1137 2020-08-06 South West 0
## 1138 2020-08-07 South West 0
## 1139 2020-08-08 South West 0
## 1140 2020-08-09 South West 0
## 1141 2020-08-10 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Tuesday 11 Aug 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.479 -6.004 -1.382 4.147 10.757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.317e+00 7.493e-02 57.61 <2e-16 ***
## note_lag 1.684e-05 7.823e-07 21.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 36.93893)
##
## Null deviance: 19108.7 on 101 degrees of freedom
## Residual deviance: 4005.2 on 100 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 74.927676 1.000017
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 64.506986 86.538480
## note_lag 1.000015 1.000018
Rsq(lag_mod)
## [1] 0.790398
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
Sys.info()
## sysname
## "Darwin"
## release
## "19.6.0"
## version
## "Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64"
## nodename
## "Mac-1597226779882.local"
## machine
## "x86_64"
## login
## "root"
## user
## "runner"
## effective_user
## "runner"6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.2 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.15
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.2.0
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.2 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.1 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.1 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.5.0 webshot_0.5.2 httr_1.4.2
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-9 zip_2.1.0 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.6 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.13.0 modelr_0.1.8 vctrs_0.3.2 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.16
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1